Who are we?
Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.
A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.
Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.
Job Summary
We are looking for a hands-on Sr. Staff Engineer to own the architecture and delivery of Network Observability’s data and ETL layer. You will build the pipelines that collect network events, logs, and metrics at scale and turn them into correlated, enriched, decision-ready data that operators, customers, APIs, and AI agents can trust. You will be the feature lead for large, ambiguous problems. You will break them into use-case-driven increments that deliver value early, and you will write code, set technical direction, and raise the engineering bar for the team.
Responsibilities
Feature Leadership
Turn large, loosely defined asks (e.g., “unified network health,” “proactive anomaly detection”) into use cases, data contracts, and phased delivery plans with clear MVPs and success metrics
Partner with product, NOC, network engineering, and API teams to prioritize by operational impact, and decide when to build, buy, or adopt open source
Write design docs, drive architecture reviews, and lead by example in code and on-call
Telemetry Ingestion & Processing
Build collection pipelines for SNMP, gNMI/streaming telemetry, syslog, and flow data on messaging platforms such as Kafka, NATS, RabbitMQ, or Pulsar
Define schemas, serialization formats, and data contracts across producers and consumers
Build real-time processing in Apache Flink (or equivalent) for normalization, enrichment, deduplication, and correlation, and batch/backfill pipelines in Apache Spark
Design for well-understood delivery semantics, late and out-of-order data, replay, and data quality checks
Insight, Correlation & Storage
Turn raw events, logs, and metrics into decision-data, correlated across devices, links, and services and enriched with topology and inventory context
Select and design fit-for-purpose storage: ClickHouse or similar OLAP stores, Druid, Prometheus with Thanos (or similar), and ELK for logs and events
Own data modeling, retention, downsampling, and cost and performance trade-offs at scale
AI-Native Development
Partner with the Network API and AI teams so MCP servers and agents get governed, high-quality, well-documented data surfaces, with freshness and reliability included
Use AI-assisted development tools in daily work and help set team practices for reviewing AI-generated code
Support building data tools for Observability MCP
Reliability & Technical Leadership
Define SLOs for pipeline freshness, completeness, and accuracy, and monitor the pipelines themselves
Lead RCA for complex issues such as data gaps, lag, cardinality explosions, and schema drift
Mentor engineers, review designs and code, and influence the multi-year data roadmap
Qualifications
Required
7+ years of software engineering experience, with significant time building large-scale data pipelines, streaming platforms, or telemetry systems
Deep, hands-on experience with at least one major streaming platform (Kafka, Pulsar, NATS, RabbitMQ)
Production experience with stream processing (Flink or similar) and batch processing (Spark)
Strong understanding of network observability data (events, logs, metrics) and protocols (SNMP, gNMI, syslog, flow data)
Experience storing and querying telemetry at scale in ClickHouse, Druid, or similar, and in Prometheus/Thanos or similar timeseries systems
Working experience with ELK/Elastic (or OpenSearch)
Strong programming skills in Java/Springboot, or Python
Daily use of AI coding assistants, and familiarity with MCP or…